{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import Series, DataFrame\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.038029\n",
       "1      0.576387\n",
       "2     -1.482785\n",
       "3      0.426277\n",
       "4      0.404734\n",
       "         ...   \n",
       "995    0.347917\n",
       "996   -1.976461\n",
       "997    1.543979\n",
       "998   -0.518783\n",
       "999   -0.448160\n",
       "Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = Series(np.random.randn(1000))\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   2.,  19.,  95., 203., 238., 252., 130.,  54.,   6.]),\n",
       " array([-4.04480174, -3.34714982, -2.64949791, -1.95184599, -1.25419408,\n",
       "        -0.55654217,  0.14110975,  0.83876166,  1.53641358,  2.23406549,\n",
       "         2.9317174 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(s1) #matplotlib的画图方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#s1.plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(s1, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(s1,fill=True, color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.plt.hist(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.rugplot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
